Introduction

• I've chosen to explore and explain the ford go-bike data :)
• The data describes the trip activity of its users.
• Have fun reading this beatifully done analysis :)

Explanatory Data Analysis

The Question we are tying to answer is what affects the duration of a trip that a user takes from the available data :)

Univariate Exploration

gender_barplot()

Apparently the majority of the users are males....

user_type_barplot()

Apparently most of the users are subscribers.....

user_birth_year_hist()

Bivariate Exploration

The first parameter we considered is whether the gender of the user affects the duration that the user takes

gender_catblot()

From the previous graph it's clear that the gender of the user is not related to the duration of the trip, since all of the three columns nearly have the same height.

The second parameter we considered is whether the user's type (Either Customer or Subscriber) affected the duration of the trip that user takes

user_type_catplot()

From the previous graph it's clear that the type of the user (Either Customer or Subscriber) is not related to the duration of the trip, since the two columns nearly have the same height.

user_birth_year_duration_plot()

There appears a teeny tiny (expected) relation betweeen age and duration of the trip since the longer durations are from users of younger age (usually).

Multivariate Exploration

user_age_gender_duration()

The most durations recorded are nearly in the range of 100 ~ 2000 Seconds by the three genders :)

Conclusions

• Most of the users are Males.
• There is no apparent relation what so ever between user's gender and the duration of the trip.
• There is no apparent relation between user's type (Either Customer or Subscriber) and the duration of the trip.
• The most durations recorded are nearly in the range of 100 ~ 2000 Seconds by the three genders :)
• There appears a teeny tiny (expected) relation betweeen age and duration of the trip since the longer durations are from users of younger age (usually).
• Most of the users are born between 1980 and 1990, which means the majority are adults between 31 to 41 years old.

Limitations

• There is no enough parameters to know which actually affects the duration of the trip.